Search Results (10)

Agent-based models provide detailed, bottom-up approaches to investigate complex socio-ecological systems. This study presents a first step towards a modular agent-based simulation that is based upon empirical data, as well as environmental suitability maps and an assessment of livestock units. To illustrate the capabilities of our simulation, we use a geographically explicit approach to simulate a component of the production of animal products of a rural settlement in the lower Bakırçay catchment, western Turkey. The model structurally couples various agent types representing several elements and processes of the animal husbandry and food production value chain, such as sedentary herders—practising daily, short-distance pastoralism—and their flocks of goats and sheep, as well as milking and slaughtering. The modelling tool captures the fundamental socio-ecological dynamics of animal husbandry and food production in rural settlements. Therefore, the tool is valuable as a basis to discuss hypotheses regarding the number of animals that are needed to cover the requirements of different growing populations. Full article

Heat demand of buildings and related CO₂ emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age. Full article

Conservation agriculture may lead to increased penetration resistance due to soil compaction. To loosen the topsoil and lower the compaction, one-time inversion tillage (OTIT) is a measure frequently used in conservation agriculture. However, the duration of the positive effects of this measure on penetration resistance is sparsely known. Therefore, the aim of this study was to analyze the spatio-temporal behavior of penetration resistance after OTIT as an indicator for soil compaction. A field subdivided into three differently tilled plots (conventional tillage with moldboard plough to 30 cm depth (CT), reduced tillage with chisel plough to 25 cm depth (RT1) and reduced tillage with disk harrow to 10 cm depth (RT2)) served as study area. In 2014, the entire field was tilled by moldboard plough and penetration resistance was recorded in the following 5 years. The results showed that OTIT reduced the penetration resistance in both RT-plots and led to an approximation in all three plots. However, after 18 (RT2) and 30 months (RT1), the differences in penetration resistance were higher (p < 0.01) in both RT-plots compared to CT. Consequently, OTIT can effectively remove the compacted layer developed in conservation agriculture. However, the lasting effect seems to be relatively short. Full article

Avoiding soil compaction is one of the objectives to ensure sustainable agriculture. Subsoil compaction in particular can be irreversible. Frequent passages by (increasingly heavy) agricultural machinery are one trigger for compaction. The aim of this work is to map and analyze the extent of traffic intensity over four years. The analysis is made for complete seasons and individual operations. The traffic intensity is distinguished into areas with more than five wheel passes, more than 5 Mg and 3 Mg wheel load. From 2014 to 2018, 63 work processes on a field were recorded and the wheel load and wheel passes were modeled spatially with FiTraM. Between 82% (winter wheat) and 100% (sugar beet) of the total infield area is trafficked during a season. The sugar beet season has the highest intensities. High intensities of more than five wheel passes and more than 5 Mg wheel load occur mainly during harvests in the headland. At wheel load ≥3 Mg, soil tillage also stresses the headland. In summary, no work process stays below one of the upper thresholds set. Based on the results, the importance of a soil-conserving management becomes obvious in order to secure the soil for agriculture in a sustainable way. Full article

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted. Full article

Location modeling, both inductive and deductive, is widely used in archaeology to predict or investigate the spatial distribution of sites. The commonality among these approaches is their consideration of only spatial effects of the first order (i.e., the interaction of the locations with the site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely considered. We introduce a deductive approach to investigating such second-order effects using linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to simulate a point distribution using expert knowledge of two distinct hunter–gatherer groups, namely, reindeer hunters and elk hunters. The modeled points and point densities were compared with the actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects of varying intensity for different periods, were applied. The results reveal differences between the two investigated groups, with the reindeer hunters showing location-related interaction patterns, indicating a spatial memory of the preferred locations over an extended period of time. Overall, this paper shows that second-order effects occur in the geographical modeling of archaeological finds and should be taken into account by using approaches such as the one presented in this paper. Full article

Soil erosion by water is one of the main soil degradation processes worldwide, which leads to declines in natural soil fertility and productivity especially on arable land. Despite advances in soil erosion modelling, the effects of compacted tramlines are usually not considered. However, tramlines noticeably contribute to the amount of soil eroded inside a field. To quantify these effects we incorporated high-resolution spatial tramline data into modelling. For simulation, the process-based soil erosion model EROSION3D has been applied on different fields for a single rainfall event. To find a reasonable balance between computing time and prediction quality, different grid cell sizes (5, 1, and 0.5 m) were used and modelling results were compared against measured soil loss. We found that (i) grid-based models like E3D are able to integrate tramlines, (ii) the share of measured erosion between tramline and cultivated areas fits well with measurements for resolution ≤1 m, (iii) tramline erosion showed a high dependency to the slope angle and (iv) soil loss and runoff are generated quicker within tramlines during the event. The results indicate that the integration of tramlines in soil erosion modelling improves the spatial prediction accuracy, and therefore, can be important for soil conservation planning. Full article

The increasing use of biogas, produced from energy crops like silage maize, is supposed to noticeably change the structures and patterns of agricultural landscapes in Europe. The main objective of our study is to quantify this assumed impact of intensive biogas production with the example of an agrarian landscape in Northern Germany. Therefore, we used three different datasets; Corine Land Cover (CLC), local agricultural statistics (Agrar-Struktur-Erhebung, ASE), and data on biogas power plants. Via kernel density analysis, we delineated impact zones which represent different levels of bioenergy-generated transformations of agrarian landscapes. We cross-checked the results by the analyses of the land cover and landscape pattern changes from 2000 to 2012 inside the impact zones. We found significant correlations between the installed electrical capacity (IC) and land cover changes. According to our findings, the landscape pattern of cropland—expressed via landscape metrics (mean patch size (MPS), total edge (TE), mean shape index (MSI), mean fractal dimension index (MFRACT)—increased and that of pastures decreased since the beginning of biogas production. Moreover, our study indicates that the increasing number of biogas power plants in certain areas is accompanied with a continuous reduction in crop diversity and a homogenization of land use in the same areas. We found maximum degrees of land use homogenisation in areas with highest IC. Our results show that a Kernel density map of the IC of biogas power plants might offer a suitable first indicator for monitoring and quantifying landscape change induced by biogas production. Full article

Societies undergo continuous dynamics and change. By investigating the spatial structure of societal remains and material culture, we tried to get insights into the processes of their landscapes creation. Ritual practices, economic strategies, or the societal structure are stored in the landscape as a form of cultural contextualization. We presumed that changes of these will be strongest during phases of transformation and investigated to which degree transformation processes are mirrored in the spatial structure of material remains. Absolute and relative locations were investigated using data from Neolithic domestic and ritual sites in Wagrien, Schleswig-Holstein, Germany. The results showed that transformations have a different influence on ritual and domestic locations: There are no discernible influences on the choice of relative domestic site locations, in contrast to ritual sites, whose relative location changes as a result of sociocultural transformations. This illustrates the importance of cultural and socioeconomic functions of individual sites and shows that transformations, even when they impact the fundamental structure of a society, do act on different relative and absolute scales and spheres. Full article

Soil compaction caused by field traffic is one of the main threats to agricultural landscapes. Compacted soils have a reduced hydraulic conductivity, lower plant growth and increased surface runoff resulting in numerous environmental issues such as increased nutrient leaching and flood risk. Mitigating soil compaction, therefore, is a major goal for a sustainable agriculture and environmental protection. To prevent undesirable effects of field traffic, it is essential to know where and when soil compaction may occur. This study developed a model for soil compaction risk assessment of arable soils at regional scale. A combination of (i) soil, weather, crop type and machinery information; (ii) a soil moisture model and (iii) soil compaction models forms the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment). The SaSCiA-model computes daily maps of soil compaction risk and associated area statistics for varying depths at actual field conditions and for entire regions. Applications with open access data in two different study areas in northern Germany demonstrated the model’s applicability. Soil compaction risks strongly varied in space and time throughout the year. SaSCiA allows a detailed spatio-temporal analysis of soil compaction risk at the regional scale, which exceed those of currently available models. Applying SaSCiA may support farmers, stakeholders and consultants in making decision for a more sustainable agriculture. Full article